This record contains satellite-sensed chlorophyll-a concentration images of the Canadian Beaufort Sea at 1.1 km resolution. The dataset consists of 276 images, aggregated into two-week composites by calculating the mean value at each pixel, comprising years 1998 through 2020.The dataset spans two ocean colour sensors, MODIS-Aqua and SeaWiFS. The Arctic Ocean Empirical algorithm was used to calculate chlorophyll-a concentration, after images were corrected for atmospheric effects using the NIR-SWIR switching algorithm, and Remote Sensing Reflectance (Rrs) were produced. A linear transform in log-10 space was applied to the chlorophyll-a concentration measured by SeaWiFS to improve its correlation with chlorophyll-a concentration measured by MODIS-Aqua.The months of October through February were excluded from these datasets as the sun angle in winter is too low (e.g., polar night) for reliable data to be acquired, and the region is mostly covered in sea ice. For further details, see Galley et al., 2022.

High-resolution false-color Landsat image composite of Canada's forested ecosystems (2022). This national image product represents the Composite to Change (C2C) proxy composite image derived from thousands of Landsat images acquired between July 1 and August 30, 2022. It is developed within the framework of Canada’s National Terrestrial Ecosystem Monitoring System (NTEMS). The overall process followed is described in (Hermosilla et al. 2016 ) with details on the generation of gap-free surface reflectance composites in ( Hermosilla et al. 2015). Following the motivation and rationale presented in White et al. (White et al. 2014), Landsat imagery is subjected to a series of processing steps to remove clouds and shadows as well as haze and other unwanted atmospheric effects. Year-on-year time series of Landsat imagery are interrogated to avoid missing values, and to ensure exhaustive spatial coverage of the national surface reflectance composites. False-colour 3-channel image (bands: shortwave infrared, SWIR1; near infrared; red)When using these data, please cite as: Hermosilla, T., M.A. Wulder, J.C. White, N.C. Coops, G.W. Hobart, L.B. Campbell, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. International Journal of Digital Earth 9(11), 1035-1054 (Hermosilla et al. 2016 ).

Administrative and territorial subdivisions of the City of Sherbrooke.attributs:ID - Unique identifierNumero - District numberName - Borough name - Borough name**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Industrial sectors.attributs:ID - Unique identifierName - Industrial park name**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

All the evaluation units of the graphic matrix of the City of Rouyn-Noranda.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

All stops in the public transport network managed by the City of Rouyn-Noranda**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The entire road network of the City of Rouyn-Noranda. Only the lanesPublics with an odonym are included.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Each pixel value corresponds to the difference (anomaly) between the mean “Best-Quality” Max-NDVI of the week specified (e.g. Week 18, 2000-2014) and the “Best-Quality” Max-NDVI of the same week in a specific year (e.g. Week 18, 2015). Max-NDVI anomalies < 0 indicate where weekly Max-NDVI is lower than normal. Anomalies > 0 indicate where weekly Max-NDVI is higher than normal. Anomalies close to 0 indicate where weekly Max-NDVI is similar to normal.

Each pixel value corresponds to the best quality maximum NDVI recorded within that pixel over the week specified. Poor quality pixel observations are removed from this product. Observations whose quality is degraded by snow cover, shadow, cloud, aerosols, and/or low sensor zenith angles are removed (and are assigned a value of “missing data”). In addition, negative Max-NDVI values, occurring where R reflectance > NIR reflectance, are considered non-vegetated and assigned a value of 0. This results in a Max-NDVI product that should (mostly) contain vegetation-covered pixels. Max-NDVI values are considered high quality and span a biomass gradient ranging from 0 (no/low biomass) to 1 (high biomass).

* This dataset is updated on a daily basis. The ‘Record Modified’ date refers to the last metadata update.This dataset contains the extent of mineral claims held in Nunavut. A mineral claim is an area of Crown Land that is selected using the Nunavut Map Selection system by an individual or mineral exploration company that holds a valid licence to prospect. This grants the individual or mineral exploration company the mineral rights to the recorded area as provided for under the Nunavut Mining Regulations, SOR/2014-69. If the holder of a mineral claim wishes to produce minerals from the claim, or to hold it for more than thirty years, the holder must apply for a lease of the claim. This digital coverage provides a record and tracking mechanism for mining exploration in Nunavut.For more information, visit https://www.rcaanc-cirnac.gc.ca/eng/1100100036000/1547749889500. Note: This is one of the four (4) datasets that describe mineral tenure in Nunavut. It includes mineral claims, mining leases, prospecting permits as well as coal exploration licences.